Neural networks have been applied in various domain including science, commerce, medicine, and industry. However, The knowledge learned by a trained neural network is difficult to understand. This paper proposes a Boolean algebra based algorithm to extract comprehensible Boolean rules from supervised feed-forward neural networks to uncover the black-boxed knowledge. This algorithm is called the...

We examine several formulations of fuzzy rule selection for the design of fuzzy rulebased classification systems in our twostage approach. The first stage is heuristic rule extraction where a large number of candidate rules are extracted. The second stage is evolutionary rule selection where fuzzy rule-based systems are constructed by choosing a small number of candidate rules. Rule selection i...

Web pages provide valuable knowledge for human comprehension in text, tables, and mathematical notations. However, the extraction and maintenance of structured rules from the Web pages are not easy tasks. To tackle these problems, we adopt the eXtensible Rule Markup Language framework. The RIML (Rule Identification Markup Language) and RSML (Rule Structure Markup Language) are two compliant rep...

Despite the highest classification accuracy in wide varieties of application areas, artificial neural network has one disadvantage. The way this Network comes to a decision is not easily comprehensible. The lack of explanation ability reduces the acceptability of neural network in data mining and decision system. This drawback is the reason why researchers have proposed many rule extraction alg...

The field of optimization and machine learning are increasingly interplayed and optimization in different problems leads to the use of machine learning approaches‎. ‎Machine learning algorithms work in reasonable computational time for specific classes of problems and have important role in extracting knowledge from large amount of data‎. ‎In this paper‎, ‎a methodology has been employed to opt...

Assessing seismic hazards involves specifying the likelihood, magnitude and location of earthquakes in a region. Predicting the seismic hazards is the first step in reducing the impact of the damage caused by an earthquake.  In this study, to fully utilize all the known parameters which may possibly affect the occurrence of earthquakes (mb ≥ 4.5); a data-driven rule-extraction method called the...

Today’s real world faces different kinds of complex optimization problems. The existing methodologies can’t cope of with such complex problems. This paper presents classification rule mining as a multi-objective problem rather than a single objective one. Multi-Objective optimization is a challenging area and focus for research. Here two modern domains of research are discussed one is swarm int...

This paper presents TEXTMARKER– a powerful toolkit for rule-based information extraction. TEXTMARKER is based on UIMA and provides versatile information processing and advanced extraction techniques. We thoroughly describe the system and its capabilities for human-like information processing and rapid prototyping of information extraction applications.

There are many methods introduced to solve the credit scoring problem such as support vector machines, neural networks and rule based classifiers. Rule bases are more favourite in credit decision making because of their ability to explicitly distinguish between good and bad applicants.In this paper multi-objective particle swarm is applied to optimize fuzzy apriori rule base in credit scoring. ...

Information extraction is initially applied for identification of desired information from natural language documents and conversion of the extracted text into a self-defined presentation. With the rapidly increasing amount of available information sources and electronic documents on the World Wide Web, information extraction is extended for identification from structured and semi-structured we...